Shopify Admin Product Affinity Cross Sell

Purpose

Applies market basket analysis to your order history to surface product pairs that customers naturally buy together. For every co-purchased pair it calculates support (how often the pair appears), confidence (given product A, how likely is B?), and lift (how much more likely than chance). The output is actionable input for product bundles, "frequently bought together" widgets, cross-sell email flows, and homepage recommendations. Read-only — no mutations are executed.

Prerequisites

• Authenticated Shopify CLI session: shopify auth login --store <domain>
• API scopes: read_orders, read_products (validator-confirmed: line item product field traverses the product graph)

Parameters

Workflow Steps

1. OPERATION: orders — query Inputs: first: 250, query: "created_at:>='<date_range_start>' created_at:<='<date_range_end>'", pagination cursor; select lineItems with product { id, title } and quantity; skip orders with a single line item Expected output: All multi-item orders in range; paginate until hasNextPage: false; build a product frequency map (product_id → order_count) and a pair frequency map ((product_a_id, product_b_id) → co_occurrence_count)

2. In-memory analysis:

• For each order with ≥ 2 distinct products, enumerate every unique unordered pair and increment the pair counter
• Compute metrics for each pair that meets min_support:
• Support = pair_count / total_orders
• Confidence A→B = pair_count / count(orders containing A)
• Confidence B→A = pair_count / count(orders containing B)
• Lift = support / (P(A) × P(B))
• Filter by min_confidence and min_lift; sort by sort_by; truncate to top_n

GraphQL Operations

# orders:query (multi-item basket analysis) — validated against api_version 2025-01
query OrdersForAffinityAnalysis($first: Int!, $after: String, $query: String) {
  orders(first: $first, after: $after, query: $query) {
    edges {
      node {
        id
        createdAt
        lineItems(first: 50) {
          edges {
            node {
              quantity
              product {
                id
                title
                tags
              }
            }
          }
        }
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}

Session Tracking

Claude MUST emit the following output at each stage. This is mandatory.

On start, emit:

╔══════════════════════════════════════════════╗
║  SKILL: product-affinity-cross-sell          ║
║  Store: <store domain>                       ║
║  Started: <YYYY-MM-DD HH:MM UTC>             ║
╚══════════════════════════════════════════════╝

After each step, emit:

[N/TOTAL] <QUERY|MUTATION>  <OperationName>
          → Params: <brief summary of key inputs>
          → Result: <count or outcome>

On completion, emit:

For format: human (default):

══════════════════════════════════════════════
OUTCOME SUMMARY
  Orders analysed:      <n>
  Unique products:      <n>
  Pairs evaluated:      <n>
  Pairs above threshold:<n>
  Date range:           <start> to <end>
  Sort by:              <lift|confidence|support>
  Errors:               0
  Output:               product_affinity_<date>.csv
══════════════════════════════════════════════

Followed by an inline ranked table of the top top_n pairs:

For format: json, emit:

{
  "skill": "product-affinity-cross-sell",
  "store": "<domain>",
  "started_at": "<ISO8601>",
  "completed_at": "<ISO8601>",
  "dry_run": false,
  "steps": [
    { "step": 1, "operation": "OrdersForAffinityAnalysis", "type": "query", "params_summary": "<date_range_start> to <date_range_end>", "result_summary": "<n> orders, <n> multi-item baskets", "skipped": false }
  ],
  "outcome": {
    "orders_analysed": 0,
    "unique_products": 0,
    "pairs_evaluated": 0,
    "pairs_above_threshold": 0,
    "date_range_start": "<date_range_start>",
    "date_range_end": "<date_range_end>",
    "sort_by": "lift",
    "results": [],
    "errors": 0,
    "output_file": "product_affinity_<date>.csv"
  }
}

Output Format

CSV file product_affinity_<YYYY-MM-DD>.csv with one row per qualifying pair:

Error Handling

Best Practices

1. Use at least 90 days of order history — short windows produce noisy lift scores because the probability denominators are small. 2. Lift > 2 is a strong bundle signal: customers are buying these products together at least twice as often as chance would predict. 3. Confidence A→B > 30% makes for a reliable "frequently bought with" widget: three in ten shoppers who buy A also buy B. 4. Filter out accessories and add-ons (like gift wrap or donation SKUs) with exclude_tags before ranking — they inflate support scores without being meaningful cross-sell pairs. 5. Use the recommendation_type column to split your output: bundle_candidate pairs are best for pre-built bundles or volume discounts; cross_sell pairs are better suited to cart upsells and post-purchase email recommendations. 6. Re-run quarterly — seasonal products enter and exit the top pairs list, and ignoring that produces stale recommendations.